Robust inversion for material parameters identification from correlated outlying observations
Why this work is in the frame
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Bibliographic record
Abstract
In this paper, a novel robust inversion method for correlated observations (RIMCO) is proposed to determine the material parameters from correlated observations under the effect of outliers and leverage points. This method is based on a full equivalent weight matrix established from the original measurement weight matrix and an adapted full weight matrix with hard rejection to outliers. This equivalent weight matrix plays key role to refine the stochastic model, while keeping the original correlation of measurements unchanged on the one hand, and ensuring simultaneously high robustness and statistical efficiency of the proposed method, on the other hand. The performance of the proposed method is demonstrated by considering a rockfill dam as an example, where the material parameters are identified from geotechnical and geodetic measurements after achievement of the construction, and during the first filling up of reservoir. Results of comparison of RIMCO with least squares and M Huber methods concerning their robustness and efficiency are presented for various configuration options.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it